IEICE Transactions on Communications
Online ISSN : 1745-1345
Print ISSN : 0916-8516
Regular Section
Broadband Direction of Arrival Estimation Based on Convolutional Neural Network
Wenli ZHUMin ZHANGChenxi WULingqing ZENG
著者情報
ジャーナル フリー

2020 年 E103.B 巻 3 号 p. 148-154

詳細
抄録

A convolutional neural network (CNN) for broadband direction of arrival (DOA) estimation of far-field electromagnetic signals is presented. The proposed algorithm performs a nonlinear inverse mapping from received signal to angle of arrival. The signal model used for algorithm is based on the circular antenna array geometry, and the phase component extracted from the spatial covariance matrix is used as the input of the CNN network. A CNN model including three convolutional layers is then established to approximate the nonlinear mapping. The performance of the CNN model is evaluated in a noisy environment for various values of signal-to-noise ratio (SNR). The results demonstrate that the proposed CNN model with the phase component of the spatial covariance matrix as the input is able to achieve fast and accurate broadband DOA estimation and attains perfect performance at lower SNR values.

著者関連情報
© 2020 The Institute of Electronics, Information and Communication Engineers
次の記事
feedback
Top